Kenneth A. Kaufman, George Mason University and Ryszard S. Michalski, George Mason University and Polish Academy of Sciences

A new approach to evolutionary computation, called Learnable Evolution Model (LEM), has been applied to the problem of optimizing tube structures of heat exchangers. In contrast to conventional Darwinian-type evolutionary computation algorithms that use various forms of mutation and/or recombination operators, LEM employs machine learning to guide the process of generating new individuals. A system, ISHED1, based on LEM, automatically searches for the highest capacity heat exchangers under given technical and environmental constraints. The results of experiments have been highly promising, often producing solutions exceeding the best human designs.

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.